University of Cambridge > Talks.cam > Inference Group > Variational Gibbs Sampling

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

Variational Gibbs Sampling

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Phil Cowans.

I introduce a MCMC method for sampling from latent variable models. The sampling scheme circumvents the traditional latent variable sample by creating a transition kernel with the required parameter posterior as its invariant distribution, hoping to smooth over local maxima and trapping states in the latent variable space. In general this kernel is not analytically tractable and I approximate it with a simpler distribution using an EM bound; I’ll also discuss methods to correct this approximate chain. Finally I’ll relate the method to two-stage Gibbs sampling, EM and variational methods.

This talk is part of the Inference Group series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

john's list Meeting the Challenge of Healthy Ageing in the 21st Century Mathworks

Other talks

'Cambridge University, Past and Present' In search of amethysts, black gold and yellow gold The Fyodorov-Bouchaud conjecture and Liouville conformal field theory Macrophage-derived extracellular succinate licenses neural stem cells to suppress chronic neuroinflammation Elizabeth Bowen's Writings of the Second World War Filling box flows in porous media 'The Japanese Mingei Movement and the art of Katazome' The Gopakumar-Vafa conjecture for symplectic manifolds Café Synthetique: Graduate Talks! EU LIFE Lecture - "Histone Chaperones Maintain Cell Fates and Antagonize Reprogramming in C. elegans and Human Cells"
 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity